Update app.py

app.py

@@ -1,27 +1,92 @@
 import gradio as gr
 from transformers import pipeline
+import pandas as pd
+import json
 
 pipe = pipeline("summarization", model="Gabriel/bart-base-cnn-xsum-swe")
 
-def main(in_text):
+def generate(in_text):
   print(in_text)
   answer = pipe(in_text, num_beams=5 ,min_length=20, max_length=120)
   print(answer)
   return answer[0]["summary_text"]
+  
+def update_history(df, in_text, gen_text ,generation_type, parameters):
+    # get rid of first seed phrase
+    new_row = [{"In_text": in_text,
+                "Gen_text": gen_text,
+                "Generation Type": generation_type,
+                "Parameters": json.dumps(parameters)}]
+    return pd.concat([df, pd.DataFrame(new_row)]) 
+    
+def generate_transformer(in_text, num_beams ,history):
+    gen_text= generate(in_text)
+    return gen_text, update_history(history, in_text, gen_text, "Transformer", {"num_beams": num_beams})
+ 
 
 with gr.Blocks() as demo:
-  gr.Markdown("""# Summarization Engine!""")
-  with gr.Row():
-    with gr.Column():
-      text1 = gr.Textbox(
-            label="Input Text",
-            lines=1,
-        )
-      output = gr.Textbox(label="Output Text")
-      b1 = gr.Button("Summarize!")
-      b1.click(main, inputs=[text1], outputs=output)
-  gr.Markdown("""#### Created by Gabriel""")
-
-
-if __name__ == "__main__":
-    demo.launch()
+    gr.Markdown("""# Summarization Engine!""")
+
+    with gr.Accordion("See Details", open=False):
+        gr.Markdown("lorem ipsum")
+
+
+    with gr.Tabs():
+        with gr.TabItem("Transformer Generation"):
+            gr.Markdown(
+                      """The default parameters for distilgpt2 work well to generate moves. Use this tab as 
+                      a baseline for your experiments.""")
+
+            with gr.Row():
+                with gr.Column(scale=4):
+                    text_baseline_transformer= gr.Textbox(lines=4,label="Input Text", placeholder="hej hej",)
+                with gr.Column(scale=3):
+                    with gr.Row():
+                        num_beams = gr.Slider(minimum=2, maximum=10, value=2, step=1, label="Number of Beams2")
+                    output_basline_transformer = gr.Textbox(label="Output Text")
+            transformer_button = gr.Button("Summarize!")            
+
+        # with gr.TabItem("Strong Baseline"):
+        #     gr.Markdown(
+        #               """The default parameters for distilgpt2 work well to generate moves. Use this tab as 
+        #               a baseline for your experiments.""")
+        #     with gr.Row():
+        #         with gr.Column(scale=4):
+        #             text_baseline= gr.Textbox(lines=4,label="Input Text", placeholder="hej hej",)
+        #         with gr.Column(scale=3):
+        #             with gr.Row():
+        #                 num_beams2 = gr.Slider(minimum=2, maximum=10, value=2, step=1, label="Number of Beams2")
+        #                 num_beams3 = gr.Slider(minimum=2, maximum=10, value=2, step=1, label="Number of Beams3")
+        #             output_basline = gr.Textbox(label="Output Text")
+        #     baseline_button = gr.Button("Summarize!")
+
+        # with gr.TabItem("LexRank"):
+        #     gr.Markdown(
+        #               """The default parameters for distilgpt2 work well to generate moves. Use this tab as 
+        #               a baseline for your experiments.""")
+        #     with gr.Row():
+        #               label="Number of Beams")
+        #         text_baseline= gr.Textbox(label="Input Text", placeholder="hej hej",)
+        #         output_basline = gr.Textbox(label="Output Text")
+        #     baseline_button = gr.Button("Summarize!")
+
+    gr.Examples([["hi", 5]], [text_baseline_transformer, num_beams])       
+    
+    with gr.Box():
+        gr.Markdown("<h3> Generation History <h3>")
+        # Displays a dataframe with the history of moves generated, with parameters
+        history = gr.Dataframe(headers=["In_text", "Gen_text", "Generation Type", "Parameters"], overflow_row_behaviour="show_ends", wrap=True)
+
+
+
+    with gr.Box():
+      	gr.Markdown("<h3>How did you make this?<h3>")
+        # gr.Markdown("""hej bottom.""")
+    transformer_button.click(generate_transformer, inputs=[text_baseline_transformer, num_beams ,history], outputs=[output_basline_transformer , history] )
+
+
+
+   # baseline_button.click(generate_transformer, inputs=[text_baseline, num_beams2 ,history], outputs=[output_basline,history] )
+
+
+demo.launch()